The accurate prediction of moisture content is crucial for controlling the drying process of agricultural products. While existing studies on drying models often rely on laboratory-scale experiments with limited data, real-time and high-frequency data collection under industrial conditions remains underexplored. This study collected and constructed a multi-dimensional dataset using an industrial-grade data acquisition system specifically designed for heat pump low-temperature circulating dryers. An artificial neural network (ANN) prediction model for moisture content during the rice drying process was developed. To prevent model overfitting, K-fold cross-validation was utilized. The model’s performance was evaluated using the mean squared error (MSE) and the coefficient of determination (R²), which also helped determine the preliminary structure of the ANN model. Bayesian regularization (trainbr) was then employed to train the network. Furthermore, optimization was conducted using neural network weights (RI) analysis and Sobol variance contribution analysis of the input variables to simplify the model structure and improve predictive performance. The experimental results showed that optimizing the model through RI sensitivity analysis simplified its topology to a 5-14-1 structure. The optimized model exhibited not only simplicity but also high prediction accuracy, achieving R² values of 0.969 and 0.966 for the training and testing sets, respectively, with MSEs of 5.6 × 10⁻³ and 6.3 × 10⁻³. Additionally, the residual errors followed a normal distribution, indicating that the predictions were reliable and realistic. Statistical tests such as t-tests, F-tests, and Kolmogorov–Smirnov tests revealed no significant differences between the predicted and actual values of rice moisture content, confirming the high consistency between them.

The advancement of agricultural mechanization necessitates precise and standardized classification based on technical characteristics to enhance green, efficient, and high-quality development. The current lack of scientific and standardized definitions and classifications for various types of agricultural machinery has become a bottleneck, complicating the machine selection and affecting the compatibility of the machinery with optimized field operations. To address this complexity, we propose a comprehensive classification method that integrates principal component analysis (PCA), cluster analysis, and the qualitative analysis of the functional components for defining and scientifically classifying harvesters. The key functional and technical properties of harvesters were analyzed, and eight primary parameters (machine weight, cutting width, feed rate, rated power, overall machine length, width, height, and working efficiency) were selected, supplemented by nine key functional components (walking mechanism, cutting device, threshing device, separating device, cleaning device, grain collecting device, grain unloading device, cabin, and track size). In the first step, principal component analysis was performed to reduce the dimensionality of the parameters, yielding three principal components with contribution rates of 41.610%, 28.579%, and 15.134%, respectively. One primary parameter from each component was selected for further analysis. In the second stage, Q-type cluster analysis classified the harvesters based on the squared Euclidean distance between the operational parameters, resulting in three classes of harvesters. Finally, functional component analysis provided detailed insights, further refining the classification into four major categories: mini, small, medium, and large harvesters. The results of this work provide a scientific basis for the definition and classification of the harvester products available on the market. This method offers a robust framework for the rational selection and planning of agricultural machinery, promoting sustainable mechanization with a focus on technical parameters and functional attributes.